Patient real-time monitoring management system based on intelligent wrist strap

Abstract

The invention discloses a patient real-time monitoring management system based on an intelligent wrist strap, which relates to the technical field of medical information and comprises a data acquisition sensing module, an anomaly detection early warning module, a health trend prediction module, a data management module and a user interaction module. According to the patient real-time monitoring management system based on the intelligent wrist strap, the accuracy of patient health monitoring and the early warning response speed are remarkably improved through a dynamic anomaly detection and personalized health trend prediction method. Through abnormal detection and dynamically-adjusted health trend modeling, sudden health problems can be recognized in real time, future health risks can be predicted in advance, and particularly for chronic disease patients and old people, a more accurate personalized health management scheme can be provided, so that the health management effect of the patients is remarkably improved, the medical cost is reduced, and the health management effect of the patients is improved. The living quality of the patient is improved.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a patient real-time monitoring and management system based on a smart wristband. Background Art

[0002] With the improvement of people's awareness of health management and the rapid development of smart wearable devices, health monitoring systems based on smart wristbands have gradually become an important tool for personal health management and medical monitoring. Existing smart wristbands generally rely on sensors to collect physiological data (such as heart rate, blood oxygen, body temperature, etc.) in real time, and analyze and display health data through simple algorithms. These devices are usually able to monitor the user's real-time health status and display and conduct preliminary analysis of the data through mobile phone applications. However, existing technologies face several important challenges and urgently need breakthroughs to achieve more efficient and accurate real-time patient monitoring and health management.

[0003] Most existing smart wristband systems rely on traditional anomaly detection methods, mainly using fixed thresholds to determine whether health status is abnormal. This method often fails to take into account individual differences and dynamic changes in patients, and can easily lead to missed or misdiagnosed conditions, especially in patients with chronic diseases or elderly patients. In addition, existing smart wristband systems lack the ability to predict health trends, and can usually only reflect real-time health data, but cannot provide patients with trend analysis of long-term health changes, thus affecting the effectiveness of prevention and intervention. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a patient real-time monitoring and management system based on a smart wristband to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a patient real-time monitoring and management system based on a smart wristband, comprising: S1. Analyze the physiological indicators of the smart wristband; S2. Based on the analysis of the physiological indicators of the smart wristband, select the type of sensor to be carried by the smart wristband; S3, determine the transmission path and communication protocol of sensor data to ensure real-time data collection from sensors to the cloud or local server; S4, perform anomaly detection and early warning based on real-time collected sensor data; S5. Based on real-time collected sensor data, analyze the patient's long-term health data and living habits, establish a personalized health trend model, and predict the future health status of patients wearing smart wristbands.

[0006] To further optimize the technical solution, in steps S1 and S2, the physiological indicators of the smart wristband include heart rate, blood pressure, body temperature, and blood oxygen saturation, and the types of sensors include heart rate sensors, blood pressure sensors, temperature sensors, and blood oxygen sensors.

[0007] To further optimize the technical solution, in step S4, an abnormality detection and early warning model is constructed, and the model combines various physiological indicators and individual differences of patients to perform abnormality detection and intelligent early warning; Define the physiological indicators monitored by the smart wristband: Heart rate , blood oxygen saturation ,blood pressure ,body temperature , then: Indicates at a point in time The collected physiological index data; Represents a physiological indicator data sequence over a period of time in the past; The anomaly detection and early warning model includes: Weighted anomaly score; Overall abnormality score; Dynamically adjust thresholds; Trigger the early warning mechanism.

[0008] To further optimize this technical solution, the anomaly detection early warning model defines the anomaly score of each physiological indicator when performing weighted anomaly scoring. As shown below: ; in, It is the current moment No. The value of physiological indexes; and are the mean and standard deviation of the physiological index in the patient's historical data, indicating the normal fluctuation range; is the weight coefficient of the indicator, which is used to indicate the importance of different indicators; The weighted average of the abnormal scores of all physiological indicators is used to obtain the overall abnormal score. : ; in, is the number of physiological indicators monitored; Overall abnormality score Used to measure the abnormal degree of various physiological indicators at the current moment; Since the health baselines of different patients vary greatly, a dynamic adjustment threshold is introduced to avoid false alarms. The calculation method of the dynamically adjusted threshold is as follows: ; in, It is a basic threshold, determined based on the statistical properties of all patient data; is an adjustment coefficient that represents the sensitivity of the model to dynamically adjust the threshold, fine-tuning it based on the patient's health history; is the overall anomaly score of the previous moment, the threshold Dynamic adjustment is performed based on the abnormal score of the previous moment.

[0009] Further optimizing this technical solution, the overall abnormality score Exceeding the threshold , it is judged as abnormal and triggers the early warning mechanism. The early warning signal will be immediately pushed to the patient or medical staff through smart devices or mobile phone applications; The urgency of the warning is based on If the value is higher than the threshold, an emergency alarm is issued; if the value is lower than the threshold, a normal alarm is issued.

[0010] Further optimizing the technical solution, in step S5, the health trend model converts physiological indicators: heart rate , blood oxygen saturation ,blood pressure ,body temperature , dynamically associated with the patient's health history, forming personalized health trends and predicting a certain moment in the future health status.

[0011] To further optimize this technical solution, the health trend model defines the historical data of each physiological indicator as: For the moment heart rate, blood pressure, blood oxygen saturation and body temperature; For each physiological index at the time The weight coefficient of Predicting future health status by weighting historical data : ; in, represents the predicted health status; Indicates The change of a physiological index, that is, the change compared with the previous day; It's time Corresponding physiological indicators The weight of It is the adjustment coefficient that controls the impact of the change on the predicted health status and is used to adjust the impact of the change trend.

[0012] To further optimize this technical solution, in the health trend model, the weight Dynamic adjustments are made to make the impact of each physiological indicator on health prediction more flexible and personalized; The dynamic weight adjustment formula is as follows: ; in, For the moment Corresponding physiological indicators The weight of It is a measure of the error in the prediction results, expressed as the difference between the model prediction value and the actual value; is the learning rate of each physiological indicator, which is used to control the sensitivity of weight update.

[0013] To further optimize the technical solution, the functional modules of the system include a data acquisition sensing module, an abnormality detection and early warning module, a health trend prediction module, a data management module and a user interaction module.

[0014] To further optimize this technical solution, in the functional modules: Data acquisition sensor module, used to collect data of physiological indicators in real time and transmit it to the cloud or local server; The anomaly detection and early warning module performs anomaly detection and early warning based on the collected data; The health trend prediction module predicts the future health status of patients wearing smart wristbands based on the collected data; Data management module, which provides data storage and security management functions; The user interaction module provides a user interaction interface to facilitate viewing of health data and warning information.

[0015] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a real-time patient monitoring and management system based on a smart wristband as described in the first aspect of the present invention are implemented.

[0016] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a real-time patient monitoring and management system based on a smart wristband as described in the first aspect of the present invention are implemented.

[0017] Compared with the prior art, the present invention provides a patient real-time monitoring and management system based on a smart wristband, which has the following beneficial effects: This patient real-time monitoring and management system based on smart wristbands significantly improves the accuracy of patient health monitoring and the speed of early warning response through dynamic anomaly detection and personalized health trend prediction methods. Through anomaly detection and dynamically adjusted health trend modeling, it can identify sudden health problems in real time and predict future health risks in advance, especially for chronic disease patients and the elderly, and can provide more accurate personalized health management solutions, thereby significantly improving the health management effect of patients, reducing medical costs, and improving the quality of life of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic diagram of the construction process of a patient real-time monitoring and management system based on a smart wristband proposed by the present invention; Figure 2 This is a schematic diagram of the functional modules of a patient real-time monitoring and management system based on a smart wristband proposed by the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. Embodiment 1

[0023] Reference Figure 1 , which is the first embodiment of the present invention, provides a patient real-time monitoring and management system based on a smart wristband, comprising: S1. Analyze the physiological indicators of the smart wristband.

[0024] In this embodiment, before developing a real-time patient monitoring and management system based on a smart wristband, an in-depth analysis of physiological indicators is first required. Through communication with medical experts, equipment manufacturers, and target users (patients), the functions that the system needs to meet are clarified. For example, the wristband needs to monitor physiological indicators such as heart rate, blood pressure, body temperature, and blood oxygen saturation in real time, and can issue an alarm in time when an abnormality occurs. In addition, it is also important to determine the goals of the system. For example, the system needs to have high-precision data acquisition capabilities, low-power design, powerful data analysis capabilities, and good user experience. The demand analysis phase should be combined with the characteristics of the target patient group, such as the elderly, patients with chronic diseases, etc., to ensure that the designed system can provide personalized health management services.

[0025] S2. Based on the analysis of the physiological indicators of the smart wristband, select the type of sensor to be carried by the smart wristband.

[0026] In this embodiment, based on the analysis of physiological indicators, selecting a suitable sensor is a crucial step. The wristband sensor needs to meet multiple requirements such as high precision, low power consumption and real-time performance.

[0027] In the steps S1 and S2, the physiological indicators of the smart wristband include heart rate, blood pressure, body temperature, and blood oxygen saturation, and the types of sensors include heart rate sensors, blood pressure sensors, temperature sensors, and blood oxygen sensors.

[0028] For example, a heart rate sensor can be based on photoplethysmography (PPG) technology, which can measure heart rate non-invasively and has low power consumption. The data acquisition design should include the interface specification between the sensor and the smart wristband main control chip.

[0029] S3. Determine the transmission path and communication protocol of sensor data to ensure real-time data collection from sensors to the cloud or local server.

[0030] In this embodiment, real-time transmission after data collection is the key to ensure that the system can intervene in a timely manner. The smart wristband needs to have stable wireless communication capabilities. Common communication methods include Bluetooth, Wi-Fi, LoRa, etc. Select the appropriate communication technology based on the needs of the target patient group and the usage environment. For example, Bluetooth is suitable for short-distance data transmission, Wi-Fi is suitable for scenarios that require real-time data transmission, and LoRa is suitable for long-distance and low-power application scenarios.

[0031] In addition, the design of the communication protocol should take into account the reliability of data transmission, using lightweight protocols such as MQTT or CoAP to ensure that data can be transmitted smoothly in various network environments. This step also needs to consider the battery life of the device, so the frequency of data transmission and the size of the data packet need to be optimized to balance the relationship between real-time performance and battery consumption.

[0032] S4. Perform anomaly detection and early warning based on real-time collected sensor data.

[0033] Anomaly detection is one of the core functions of the system. In order to promptly detect possible health problems of patients, such as cardiac arrest and breathing difficulties, the system needs to design a model to analyze data in real time and make judgments.

[0034] In this embodiment, an abnormality detection and early warning model is constructed, and the model combines various physiological indicators and individual differences of patients to perform abnormality detection and intelligent early warning; Define the physiological indicators monitored by the smart wristband: Heart rate , blood oxygen saturation ,blood pressure ,body temperature , then: Indicates at a point in time The collected physiological index data; Represents a data sequence of physiological indicators over a period of time.

[0035] The anomaly detection and early warning model includes: Weighted anomaly score; Overall abnormality score; Dynamically adjust thresholds; Trigger the early warning mechanism.

[0036] Furthermore, the anomaly detection and early warning model defines the anomaly score of each physiological indicator when performing weighted anomaly scoring. As shown below: ; in, It is the current moment No. The value of physiological indicators (such as heart rate, blood oxygen, etc.); and are the mean and standard deviation of the physiological index in the patient's historical data, indicating the normal fluctuation range; is the weight coefficient of the indicator, which is used to indicate the importance of different indicators. Different physiological indicators are more sensitive to early warning of certain health risks, so their weight coefficients are different.

[0037] The weighted average of the abnormal scores of all physiological indicators is used to obtain the overall abnormal score. : ; in, is the number of physiological indicators monitored; Overall abnormality score Used to measure the abnormal degree of various physiological indicators at the current moment.

[0038] In order to take into account the individual differences and health history of patients, and because the health baselines of different patients vary greatly, a dynamically adjusted threshold is introduced to avoid false alarms and ensure the accuracy of the warning. The dynamically adjusted threshold is calculated as follows: ; in, It is a basic threshold, determined based on the statistical properties of all patient data; is an adjustment coefficient that represents the sensitivity of the model to dynamically adjust the threshold, fine-tuning it based on the patient's health history; is the overall anomaly score of the previous moment, the threshold Dynamic adjustment is performed based on the abnormal score of the previous moment.

[0039] The overall abnormality score Exceeding the threshold , it is judged as abnormal and triggers the early warning mechanism. The early warning signal will be immediately pushed to the patient or medical staff through smart devices or mobile phone applications; The urgency of the warning is based on If the value is higher than the threshold, an emergency alarm is issued; if the value is lower than the threshold, a normal alarm is issued.

[0040] When the model is in use, each time a new round of data is obtained from the wristband, the system will calculate the abnormal score of each physiological indicator. , and update the overall anomaly score The system will dynamically adjust the sensitivity of the warning according to the patient's historical data and real-time health status to ensure that there will not be too many false alarms. If the set threshold is exceeded, the system will trigger an alert to notify the patient or medical staff to ensure timely intervention.

[0041] This model can be adjusted according to individual differences of patients to avoid false alarms and improve warning accuracy. It is a key component of the smart wristband patient real-time monitoring and management system.

[0042] S5. Based on real-time collected sensor data, analyze the patient's long-term health data and living habits, establish a personalized health trend model, and predict the future health status of patients wearing smart wristbands.

[0043] In this embodiment, the health trend model combines physiological indicators: heart rate , blood oxygen saturation ,blood pressure ,body temperature , dynamically associated with the patient's health history, forming personalized health trends and predicting a certain moment in the future health status.

[0044] Furthermore, the health trend model defines the historical data of each physiological indicator as: For the moment heart rate, blood pressure, blood oxygen saturation and body temperature; For each physiological index at the time The weight coefficients are dynamically adjusted according to the individual conditions of each patient (such as health status, medical history, etc.).

[0045] Predicting future health status by weighting historical data : ; in, represents the predicted health status; Indicates The change of a physiological index, that is, the change compared with the previous day; It's time Corresponding physiological indicators The weight of It is the adjustment coefficient that controls the impact of the change on the predicted health status and is used to adjust the impact of the change trend.

[0046] In the health trend model, the weight Dynamic adjustments are made to make the impact of each physiological indicator on health prediction more flexible and personalized; the weight of each physiological indicator is automatically adjusted based on historical health data and real-time monitoring feedback to adapt to the patient's health changes. For example, in certain special periods (such as patients with heart disease before a heart attack), the weight of heart rate may be higher than usual.

[0047] The dynamic weight adjustment formula is as follows: ; in, For the moment Corresponding physiological indicators The weight of It is a measure of the error in the prediction results, which can usually be expressed as the difference between the model prediction value and the actual value. Specifically, if the deviation of the predicted health status is large, it means that the influence of the physiological indicator on the current prediction may need to be increased; is the learning rate of each physiological indicator, which is used to control the sensitivity of weight update.

[0048] When the model is in use, it collects the patient's health data (heart rate, blood pressure, blood oxygen, body temperature, etc.) and environmental information (such as activity level, sleep status, etc.) from smart wristbands and other sensors. The model is trained using the patient's historical health data to obtain the initial weight of each physiological indicator. . Continuously monitor the patient's health status through wristbands and other devices, and predict future health status based on historical data and real-time data. Adjust the weight coefficient based on real-time data and error feedback , optimize prediction accuracy. Use the model to predict the health status at a certain moment in the future , and determine whether it exceeds the health risk threshold, thereby triggering an early warning.

[0049] According to the patient's individual historical health data and real-time data, the weight of each physiological indicator is dynamically adjusted to predict future health trends. In this way, the system can not only identify sudden health problems in a timely manner, but also provide a scientific basis for long-term health management, and achieve more personalized health prediction and early warning. Embodiment 2

[0050] Reference Figure 2 , which is the second embodiment of the present invention, provides functional modules of a patient real-time monitoring and management system, and the functional modules of the system include a data acquisition sensing module, an abnormality detection and warning module, a health trend prediction module, a data management module and a user interaction module.

[0051] In the functional module: Data acquisition sensor module, used to collect data of physiological indicators (such as heart rate, blood pressure, blood oxygen, body temperature, etc.) in real time and transmit it to the cloud or local server; The anomaly detection and early warning module performs anomaly detection and early warning based on the collected data; The health trend prediction module predicts the future health status of patients wearing smart wristbands based on the collected data; The data management module provides data storage and security management functions. It is responsible for storing patients' historical health data, real-time monitoring data, model parameters and other information, and ensuring the security, integrity and accessibility of the data. The user interaction module provides a user interaction interface to facilitate viewing of health data and warning information. Through the graphical interface, users can easily view real-time health data, historical trends, health warnings, forecast results and other information. Embodiment 3

[0052] This embodiment also provides a computer device, which is suitable for a real-time patient monitoring and management system based on a smart wristband, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a real-time patient monitoring and management system based on a smart wristband as proposed in the above embodiment.

[0053] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a patient real-time monitoring and management system based on a smart wristband as proposed in the above embodiment is implemented.

[0054] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0055] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0056] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0057] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0058] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A patient real-time monitoring and management system based on a smart wristband, characterized in that: include: S1. Analyze the physiological indicators of the smart wristband; S2. Based on the analysis of the physiological indicators of the smart wristband, select the type of sensor to be carried by the smart wristband; S3, determine the transmission path and communication protocol of sensor data to ensure real-time data collection from sensors to the cloud or local server; S4, perform anomaly detection and early warning based on real-time collected sensor data; S5. Based on real-time collected sensor data, analyze the patient's long-term health data and living habits, establish a personalized health trend model, and predict the future health status of patients wearing smart wristbands.

2. A patient real-time monitoring and management system based on a smart wristband according to claim 1, characterized in that: In the steps S1 and S2, the physiological indicators of the smart wristband include heart rate, blood pressure, body temperature, and blood oxygen saturation, and the types of sensors include heart rate sensors, blood pressure sensors, temperature sensors, and blood oxygen sensors.

3. The patient real-time monitoring and management system based on a smart wristband according to claim 1 is characterized in that: In step S4, an abnormality detection and early warning model is constructed, and the model combines various physiological indicators and individual differences of patients to perform abnormality detection and intelligent early warning; Define the physiological indicators monitored by the smart wristband: Heart rate , blood oxygen saturation ,blood pressure ,body temperature , then: Indicates at a point in time The collected physiological index data; Represents a physiological indicator data sequence over a period of time in the past; The anomaly detection and early warning model includes: Weighted anomaly score; Overall abnormality score; Dynamically adjust thresholds; Trigger the early warning mechanism.

4. A patient real-time monitoring and management system based on a smart wristband according to claim 3, characterized in that: The anomaly detection and early warning model defines the anomaly score of each physiological indicator when performing weighted anomaly scoring. As shown below: ； in, It is the current moment No. The value of physiological indexes; and are the mean and standard deviation of the physiological index in the patient's historical data, indicating the normal fluctuation range; is the weight coefficient of the indicator, which is used to indicate the importance of different indicators; The weighted average of the abnormal scores of all physiological indicators is used to obtain the overall abnormal score. : ； in, is the number of physiological indicators monitored; Overall abnormality score Used to measure the abnormal degree of various physiological indicators at the current moment; Since the health baselines of different patients vary greatly, a dynamic adjustment threshold is introduced to avoid false alarms. The calculation method of the dynamically adjusted threshold is as follows: ； in, It is a basic threshold, determined based on the statistical properties of all patient data; is an adjustment coefficient that represents the sensitivity of the model to dynamically adjust the threshold, fine-tuning it based on the patient's health history; is the overall anomaly score of the previous moment, the threshold Dynamic adjustment is performed based on the abnormal score of the previous moment.

5. A patient real-time monitoring and management system based on a smart wristband according to claim 4, characterized in that: The overall abnormality score Exceeding the threshold , it is judged as abnormal and triggers the early warning mechanism. The early warning signal will be immediately pushed to the patient or medical staff through smart devices or mobile phone applications; The urgency of the warning is based on If the value is higher than the threshold, an emergency alarm is issued; if the value is lower than the threshold, a normal alarm is issued.

6. The patient real-time monitoring and management system based on a smart wristband according to claim 1 is characterized in that: In step S5, the health trend model converts physiological indicators: heart rate , blood oxygen saturation ,blood pressure ,body temperature , dynamically associated with the patient's health history, forming personalized health trends and predicting a certain moment in the future health status.

7. A patient real-time monitoring and management system based on a smart wristband according to claim 6, characterized in that: The health trend model defines the historical data of each physiological indicator as: For the moment heart rate, blood pressure, blood oxygen saturation and body temperature; For each physiological index at the time The weight coefficient of Predicting future health status by weighting historical data : ； in, represents the predicted health status; Indicates The change of a physiological index, that is, the change compared with the previous day; It's time Corresponding physiological indicators The weight of It is the adjustment coefficient that controls the impact of the change on the predicted health status and is used to adjust the impact of the change trend.

8. The patient real-time monitoring and management system based on a smart wristband according to claim 7 is characterized in that: In the health trend model, the weight Dynamic adjustments are made to make the impact of each physiological indicator on health prediction more flexible and personalized; The dynamic weight adjustment formula is as follows: ； in, For the moment Corresponding physiological indicators The weight of It is a measure of the error in the prediction results, expressed as the difference between the model prediction value and the actual value; is the learning rate of each physiological indicator, which is used to control the sensitivity of weight update.

9. The patient real-time monitoring and management system based on a smart wristband according to claim 1, characterized in that: The functional modules of the system include data acquisition sensing module, abnormality detection and warning module, health trend prediction module, data management module and user interaction module.

10. A patient real-time monitoring and management system based on a smart wristband according to claim 9, characterized in that: In the functional module: Data acquisition sensor module, used to collect data of physiological indicators in real time and transmit it to the cloud or local server; The anomaly detection and early warning module performs anomaly detection and early warning based on the collected data; The health trend prediction module predicts the future health status of patients wearing smart wristbands based on the collected data; Data management module, which provides data storage and security management functions; The user interaction module provides a user interaction interface to facilitate viewing of health data and warning information.

Images